Rasa Platform v0.16 is compatible with Rasa NLU 0.13.3 or higher and Rasa Core 0.11.4 or higher.
Rasa Core 0.11 introduced some relatively large changes to the HTTP API, though migrating projects
written in python is very easy. There are very few changes to your code required, see the
Rasa Core migration guide for details.

Once you have updated your custom app code (more details on how to start the action server here),
re-run the installation script.

If you have a custom installation, or don’t want to use the installation script, you need to:

update your docker-compose / kubernetes/ openshift configuration. Here is the current version. We have added 3 containers: a new event-service, a RabbitMQ instance and a separate logger container for managing logs.

In previous versions of Rasa Core, the server running Rasa Core was also responsible for executing custom
action code. As of Rasa Core 0.11, custom actions are executed by calling a webhook provided by the user.
This enables Rasa Core to be used in a microservice architecture.

For python projects, all of this has been automated with the rasa_core_sdk package.

For projects using the HTTP api (e.g. with custom actions implemented in a language other than python),
Rasa Core 0.11 makes your app considerably simpler. The /parse and /continue endpoints have
been removed, and Rasa Core directly calls a webhook (provided by you) to execute custom actions.

This also means that you can edit your custom action code without restarting the Rasa Core server, and
the app / server / docker container running your custom actions only depends on the tiny rasa code SDK package,
and doesn’t need to have tensorflow and other Rasa Core dependencies installed.

Rasa Core and NLU models are now both managed using a unified models API.
You can push models to the server for both NLU and Core, see this page.

Rasa Core and NLU will poll Rasa Platform to request the model to run (the one tagged as active).
This means that if you are running multiple Core & NLU instances, each will be automatically updated
when you tag a new model as active.

Rasa Platform will migrate your existing models to the new API when you start up the new version, but most models will be
marked as ‘not compatible’ because of the major version upgrade to Rasa Core & NLU. Re-train your models locally and push them
using the api. Or in the case of NLU, you can also re-train using the button in the interface.